Bistable by Construction: Wall-Clock-Calibrated State Monitors Have No Moment-Detection Regime at Agent Cadence
arXiv:2606.19386v1 Announce Type: cross Abstract: Runtime monitors for autonomous agents commonly threshold an accumulated internal state - a behavioural baseline, a drift statistic, or, in our prior work, a modelled affective state. We previously reported a State Saturation Trap: threshold-on-state triggers over a continuous affect engine become near-constant alarms on SWE-bench debugging agents (Modgil 2026). A post-release audit found the engine received dt=0 between actions, so its exponential decay never operated: the published trap is a pure-accumulator result. We correct the record (err
This paper corrects a previous finding, highlighting a critical flaw in agent monitoring systems that was exposed by practical application in debugging agents.
Reliable runtime monitoring is essential for the safe and effective development and deployment of autonomous AI agents, making this error correction vital for their advancement.
Our understanding of state monitoring in AI agents is refined, emphasizing the importance of correct temporal handling and avoiding 'pure-accumulator' traps in behavioural baselines.
- · AI agent developers
- · Auditors and QA specialists
- · AI safety researchers
- · Developers relying on flawed monitoring implementations
- · Systems with uncorrected 'State Saturation Trap'
Increased scrutiny on the design and implementation of internal state monitors for AI agents.
Improved robustness and reliability of autonomous agent systems through more accurate state awareness and error detection.
Accelerated development and adoption of AI agents in sensitive applications due to enhanced trust in their monitoring capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG